Dreaming big is in our DNA. It’s who we are as a company. It’s our culture. It’s our heritage. And more than ever, it’s our future. A future where we’re always looking forward. Always serving up new ways to meet life’s moments. A future where we keep dreaming bigger. We look for people with passion, talent, and curiosity, and provide them with the teammates, resources and opportunities to unleash their full potential. The power we create together – when we combine your strengths with ours – is unstoppable. Are you ready to join a team that dreams as big as you do?
Core Responsibilities
ML Engineers manage the end-to-end lifecycle of machine learning models, ensuring they work reliably in real-world applications.
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- Model Development: Research, design, and implement appropriate ML algorithms and tools for specific business needs.
- Data Pipeline Construction: Build and maintain automated pipelines to ingest, clean, and process large datasets for training.
- Production Deployment (MLOps): Use containerization tools like Docker and orchestration platforms like Kubernetes to deploy models as scalable APIs.
- System Optimization: Perform statistical analysis and fine-tuning to improve model accuracy and computational efficiency.
- Monitoring & Maintenance: Track model performance post-deployment, detect "data drift," and implement automated retraining strategies.
- Collaboration: Act as a bridge between Data Scientists (who focus on insights) and Software Engineers (who focus on infrastructure)